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All Journal Jurnal Online Mahasiswa (JOM) Bidang Ilmu Ekonomi JOURNAL OF APPLIED INFORMATICS AND COMPUTING Journal of Applied Data Sciences Jurnal Informatika Ekonomi Bisnis Seminar Nasional Riset dan Teknologi (SEMNAS RISTEK)
Tri Putri Lestari, Tri Putri
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Other

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PENGARUH TEKNOLOGI INFORMASI, PENGENDALIAN INTERN, DAN GAYA KEPEMIMPINAN TERHADAP KINERJA INSTANSI PEMERINTAH (Studi Empiris Pada SKPD Kabupaten Bengkalis) Lestari, Tri Putri; -, Kennedy; Wiguna, Meilda
Jurnal Online Mahasiswa (JOM) Bidang Ilmu Ekonomi Vol 2, No 2 (2015): Wisuda Oktober 2015
Publisher : Jurnal Online Mahasiswa (JOM) Bidang Ilmu Ekonomi

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The purpose of this sstudy is technology information, internal control and leadership style of the performance of government agencies at SKPDs Bengkalis Regency. The research method used is descriptive research method with survey approach, the type of data used iss primary data, primary data by sending questionnaires directly to the respondents and take it back after a predetermined time period. The study population is SKPDs Bengkalis Regency. The sample was echelons III and IV on bodies and agencies in Bengkalis Regency. The classical assumption used is the test for normality, multicollinearity test and test heterokedastisitas. Hypothesis testing using Adjusted R square, Model analysis of the data used I multiple linear regression. Testing the quality of the data used are validity and reliability testing. Simultaneouss significant (Test-f), partial significance (Test-t), the hypothesis testing results show that the hypothesis first, second and third received, this suggests that information technology, internal control and leadership style of the performance of government agencies at SKPDs Bengkalis Regency.Keywords: Information Tecnhology, Internal Control,Leadership style and performance of government agencies.
Analisis Text Mining pada Sosial Media Twitter Menggunakan Metode Support Vector Machine (SVM) dan Social Network Analysis (SNA) Lestari, Tri Putri
Jurnal Informatika Ekonomi Bisnis Vol. 4, No. 3 (September 2022)
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (503.663 KB) | DOI: 10.37034/infeb.v4i3.146

Abstract

Online loans are growing rapidly in Indonesia in the last two years. This is because the online loan administration requirements are easier compared to bank financial service loans. Online loans are financial services that provide online-based services. Along with the development of online loans, many illegal online loans have sprung up and often commit violations, such as leaking customer personal information and abusing data by carrying out extreme actions such as terrorizing customers who make online loan transactions. This certainly gets a lot of comments from the public, especially on social media twitter. This study aims to conduct a sentiment analysis to see what phenomena are happening among the public regarding online loans. The data used are tweets or retweets from Twitter social media with #pinjamanonline #pinjol. Twitter social media was chosen because an incident can become a phenomenon if it gets a lot of attention from the community, especially on Twitter social media. In this study, using text mining techniques by applying the Support Vector Machine algorithm to classify sentiments on twitter users regarding online loans. This study also looks at the interactions that occur on social media Twitter using social network analysis (SNA). the results of research and testing of the Support Vector Machine method to classify online loans with an Accuracy value level of 86.6%, with a positive precision of 86%, neutral of 1.00% and negative of 87%, positive recall of 90%, neutral 87% and negative of 26 % and positive F1-Score of 88% neutral 42% and negative 86%. Then at the Social Network Analysis stage there is the most influential account, namely influencer @alvinline21 with 1402 nodes.
Improved Performance of Hybrid GRU-BiLSTM for Detection Emotion on Twitter Dataset Anam, M. Khairul; Munawir, Munawir; Efrizoni, Lusiana; Fadillah, Nurul; Agustin, Wirta; Syahputra, Irwanda; Lestari, Tri Putri; Firdaus, Muhammad Bambang; Lathifah, Lathifah; Sari, Atalya Kurnia
Journal of Applied Data Sciences Vol 6, No 1: JANUARY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i1.459

Abstract

This study addresses emotion detection challenges in tweets, focusing on contextual understanding and class imbalance. A novel hybrid deep learning architecture combining GRU-BiLSTM with SMOTE is proposed to enhance classification performance on an Israel-Palestine conflict dataset. The dataset contains 40,000 tweets labeled with six emotions: anger, disgust, fear, joy, sadness, and surprise. SMOTE effectively balances the dataset, improving model fairness in detecting minority classes. Experimental results show that the GRU-BiLSTM hybrid with an 80:20 data split achieves the highest accuracy of 89%, surpassing BiLSTM alone, which obtained 88%, and other state-of-the-art models. Notably, the proposed model delivers significant improvement in detecting the emotion of joy (recall: 0.87, F1-score: 0.86). In contrast, the surprise category remains challenging (recall: 0.24). Compared to existing research, this study highlights the effectiveness of combining SMOTE and hybrid GRU-BiLSTM, outperforming models such as CNN, GRU, and LSTM on similar datasets. The incorporation of GloVe embeddings enhances contextual word representations, enabling nuanced emotion detection even in sarcastic or ambiguous texts. The novelty lies in addressing class imbalance systematically with SMOTE and leveraging GRU-BiLSTM's complementary strengths, yielding superior performance metrics. This approach contributes to advancing emotion detection tasks, especially in conflict-related social media data, by offering a robust, context-sensitive, and balanced classification method.
Rancang Bangun Aplikasi Menu Interaktif Restoran Berbasis Android Lestari, Tri Putri; Sari, Atalya Kurnia; Ali, Haidar; Kinanti, Dewi Sekar; Larasati, Agnes Indri Pontias; Anjani, Dewi; Anggraeni, Widya Dewi
Seminar Nasional Riset dan Inovasi Teknologi (SEMNAS RISTEK) Vol 9, No 1 (2025): SEMNAS RISTEK 2025
Publisher : Universitas Indraprasta PGRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/semnasristek.v9i1.8079

Abstract

Perkembangan teknologi informasi telah memberikan dampak signifikan dalam berbagai bidang, termasuk industri kuliner. Restoran modern semakin beralih ke solusi digital untuk meningkatkan pengalaman pelanggan dan efisiensi operasional. Penelitian ini bertujuan untuk merancang dan membangun sebuah aplikasi menu interaktif berbasis Android yang memungkinkan pelanggan melihat daftar menu, melakukan pemesanan, dan berinteraksi dengan sistem restoran secara lebih efisien. Metode yang digunakan pada penelitian ini Metode penelitian grounded yang merupakan metode berdasarkan fakta yang bertujuan untuk menetapkan konsep, mengembangkan teori, serta mengumpulkan, menganalisis data pada saat yang bersamaan Hasil dari penelitian ini menunjukkan bahwa aplikasi menu interaktif berbasis Android dapat meningkatkan efisiensi pelayanan, mengurangi kesalahan dalam pemesanan, serta memberikan pengalaman yang lebih modern dan menarik bagi pelanggan. Penggunaan teknologi ini diharapkan dapat membantu restoran dalam meningkatkan kepuasan pelanggan serta mendukung digitalisasi di industri kuliner
Perancangan Model Evaluasi Kinerja untuk Peningkatan Produktivitas Karyawan Restoran Sutrisno, Sutrisno; Lestari, Tri Putri; Maheswari, Denisha Aulia; Quswa, Ahmad Darojatul; Nurhayan, Ilyas; Khairi, Muhammad Qalbi
Seminar Nasional Riset dan Inovasi Teknologi (SEMNAS RISTEK) Vol 9, No 1 (2025): SEMNAS RISTEK 2025
Publisher : Universitas Indraprasta PGRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/semnasristek.v9i1.8081

Abstract

Sumber daya manusia yang berkualitas sangat penting untuk meningkatkan produktivitas kerja suatu organisasi. Kompetensi yang tinggi pada karyawan akan mendukung peningkatan kinerja mereka. Penilaian kinerja diperlukan untuk mengevaluasi prestasi karyawan. Resto Omah Ingkung menghadapi masalah karena belum memiliki format standar untuk penilaian kinerja karyawan. Untuk mengatasi masalah ini, diperlukan sebuah sistem untuk membantu manajer dalam menentukan karyawan dengan kinerja terbaik dan memberikan masukan untuk pengembangan berdasarkan nilai kinerja karyawan. Penelitian ini mengembangkan model evaluasi kinerja karyawan berbasis logika fuzzy. Model ini menggunakan kriteria kehadiran, pelayanan, penampilan, kerjasama, dan tanggung jawab untuk membantu manajer mengidentifikasi karyawan berkinerja terbaik dan memberikan umpan balik yang berguna untuk pengembangan karyawan. Hasil penelitian menunjukkan bahwa model ini memiliki tingkat akurasi 98,19%.
Analyzing the Impact of Artificial Intelligence on Student Learning: A Case Study of SMK Tri Arga 2 Lestari, Tri Putri
Journal of Applied Informatics and Computing Vol. 9 No. 1 (2025): February 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i1.8902

Abstract

The use of artificial intelligence (AI) technology in every aspect of life is a solution that provides an important contribution to the continuity of the wheel of life, not to mention in the world of education. In this digital era, AI has become an important partner and tool for students in completing assignments and assisting in carrying out their learning activities, especially in completing assignments given by teachers. The purpose of this study is to analyze the effect of artificial intelligence on learning, especially for students of SMK Tri Arga 2. The formulation of the research problem involves a description of the use of AI in the process of completing assignments, the benefits and challenges experienced by students, and the most dominant AI applications used in completing these assignments. The research method used is a quantitative descriptive method using a questionnaire, Likert Scale, which is created on Google Form and then sent to the Whatsapp Group and student personal chat. Using a random method. 105 students of SMK Tri Arga 2 participated in this study. the results of this study showed that 51.4% stated that they often use AI. Then 52.4% or 55 students answered that they often use AI in completing school assignments. These results certainly provide a new perspective on the role of AI in helping students complete the school assignments they have been given. It is also hoped that the results of this research can help Senior High Schools (SMA) and Vocational High Schools (SMK) in improving the quality of education and learning by integrating AI more effectively in their students' academic processes, improving supervision and regulations regarding the use of AI in completing assignments and making AI a companion tool while still paying attention to the ethics of plagiarism and the growth of students' skill development.
Co-Authors Ali, Haidar Anam, M Khairul Anggraeni, Widya Dewi Dewi Anjani, Dewi Efrizoni, Lusiana Firdaus, Muhammad Bambang Irwanda Syahputra Kennedy -, Kennedy Khairi, Muhammad Qalbi Kinanti, Dewi Sekar Larasati, Agnes Indri Pontias Lathifah, Lathifah Maheswari, Denisha Aulia Meilda Wiguna Munawir Munawir Nurhayan, Ilyas Nurul fadillah, Nurul Quswa, Ahmad Darojatul Sari, Atalya Kurnia Sutrisno, Sutrisno Wirta Agustin